{"id":"W4393371631","doi":"10.2139/ssrn.4780442","title":"Wavelength-Modulated Photoacoustic Spectroscopic Instrumentation System for Multiple Greenhouse Gas Detection and In-Field Application in the Qinling Mountainous Region of China","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Spectroscopy and Laser Applications","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Photoacoustic imaging in biomedicine; Instrumentation (computer programming); Wavelength; China; Greenhouse gas; Materials science; Field (mathematics); Photoacoustic spectroscopy; Remote sensing; Environmental science; Optoelectronics; Optics; Physics; Geography; Geology; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005346799,0.0004120219,0.0003897719,0.0007246303,0.001081933,0.0002747288,0.0009236246,0.0004122983,0.0009617895],"category_scores_gemma":[0.0001829982,0.000211167,0.0002064002,0.0006882669,0.00030213,0.0004659411,0.0004887412,0.0002839757,0.0001863631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00059109,"about_ca_system_score_gemma":0.001472858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01921658,"about_ca_topic_score_gemma":0.0267227,"domain_scores_codex":[0.9997366,0.000029718,0.000005614592,0.00009375725,0.00009239333,0.00004196636],"domain_scores_gemma":[0.9998254,0.00002415372,0.00001835593,0.00001325068,0.00009176723,0.0000270957],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003997126,0.0001993952,0.05304203,0.0003125991,0.00008035662,0.000411197,0.0008882356,0.005322949,0.872061,0.0004993391,0.002148189,0.06463508],"study_design_scores_gemma":[0.0002519148,0.00153121,0.4006247,0.00004238917,0.0004741231,0.0008557088,0.001487886,0.1201932,0.4565359,0.0009132536,0.01686058,0.0002289941],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9654163,0.0004201767,0.03049192,0.0002130457,0.00005662834,0.00007738834,0.0004978043,0.0006796629,0.002147223],"genre_scores_gemma":[0.9832733,0.0001116156,0.01500933,0.00006174151,0.0000274679,0.00005307913,0.0001819198,0.00001909741,0.001262562],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01921658,"threshold_uncertainty_score":0.03820944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007432216554705503,"score_gpt":0.2500884252535649,"score_spread":0.2426562086988594,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}